Indian Institute of Public Administration

Aerosols affect the worldwide plant environment in both beneficial and harmful ways. Despite its potential importance, its direct influence on plant–water interactions is little known. Tomato plants were grown in ambient urban air, filtered air, and severely polluted air following precise exposure procedures. While measuring transpiration rate and stomatal density, leaf hydration kinetics, microscopic leaf wetness creation, and aerosol deposition patterns were also assessed.The experiment was conducted from August 2025 in three plant-growing chambers at IIT Delhi. Temperature and RH were the same. At plant level, plants were exposed to natural daylight (up to 1500 µmol m⁻² s⁻¹). Leaf dust deposition was monitored. Every other day, elevated chambers were sprayed with dust, while HEPA filters cleaned air in filtered chambers. PM2.5 deposition on leaves ranges from 50 µg/cm² to 600 µg/cm² for HEPA filter-equipped and increased PM conc. chambers, respectively. During the monitoring period, PM2.5 levels at several locations in the area averaged 150-600 µgm⁻³.Net photosynthesis, stomatal conductance, and transpiration rate were measured in real time using LI-COR 6400XT.The mass accumulated on leaves was 10 to 12 times more in the elevated PM chamber. Fresh leaves from plants grown under reduced, ambient, and elevated chamber conditions were collected, affixed to specimen holders using adhesive Leit tabs, and analysed using environmental scanning electron microscopy. Stomatal density was seen to have risen (~170 per mm²) from the seedling stage. The minimum leaf conductance (gmin) was measured on leaflets. Photosynthetic rate increased from 14 µmol m⁻² s⁻¹ to 21 µmol m⁻² s⁻¹. The gmin is anticipated to rise when dust deposition on leaves increases. The rise in water uptake by plants suggests that phenomena such as hydraulic activation of stomata (HAS) or heat retention by deposited aerosols have intensified water loss, either through cooling themselves from the heat absorbed from excessive dust accumulation or by forming wicks into the leaves from the salts in the aerosols, thereby facilitating the escape of water from leaves into the environment through evaporation. The Fv/Fm ratio, a measure of photosynthetic efficiency, was maximised in the lowered chamber.The impact of aerosols on plants is contingent upon their composition, species, and environmental conditions, affecting the movement of water via stomata and cuticular transpiration. Research indicates that ambient aerosol deposition in polluted urban environments elevates gmin, transpiration rates and modified stomatal density. Severity of impact increases pollution levels and hygroscopic aerosols due to extended exposure. Aerosol-induced water loss diminishes stomatal regulation, impairs drought resilience and water usage efficiency, and complicates carbon-water flow scaling. The increased transpiration rate leads to greater water consumption by plants, which might contribute to the depletion of groundwater levels in the IGP India. Additional investigation is required to elucidate the processes connecting aerosol deposition and stomatal response, considering their significance for global climate change.

The increasing complexity of traffic patterns in modern urban environments necessitates intelligent and scalable solutions for real-time traffic forecasting within the Vehicular Ad Hoc Networks (VANETs). The proposed hybrid lightweight Deep Neural Network (DNN)–Convolutional Neural Network (CNN) framework integrates with Geographic Routing Protocols (GRP) to enable efficient traffic prediction in resource-constrained V2X environments. The model combines the spatial feature extraction ability of CNNs with the temporal learning strength of DNNs to capture the dynamic vehicular patterns. Evaluated using the TiHAN-V2X dataset, the model achieves an MSE of 0.0141, an MAE of 0.0951, and an R $$^{2}$$ of 0.9680 that surpassing state-of-the-art baselines. The lightweight design ensures low-latency inference suitable for edge devices such as RSUs and OBUs. The contributions include a scalable hybrid architecture for spatio-temporal traffic learning, integration of geographic routing for congestion-aware communication, and validation using real-world V2X data. The results demonstrate that the proposed framework enhances predictive accuracy, reduces latency, and supports intelligent traffic management in next-generation vehicular networks.

BUMDes refers to business entities that are wholly or partially owned by the village through direct participation to manage assets, services, and other businesses for the welfare of the community. The business owned by Anyar Village is bottled drinking water sourced directly from springs in the village, while Cikedung Village does not yet have a business and their BUMDes is still inactive. The purpose of this research is to determine the role of BUMDes in increasing the income and welfare of rural communities in Anyar and Cikedung villages. Using a qualitative research method, researchers conducted interviews and direct observation in both villages using a community economic development program approach. They concluded that BUMDes in Anyar village has played a role and contributed to village income and the welfare of the village community. Later, the success strategy of the Anyar Village’s BUMDes can be applied to the Cikedung Village’s BUMDes so that they can run actively and sustainably.

This paper provides a strong platform for heart disease prediction by using ensemble machine learning models, which improves the diagnosis's accuracy and efficiency heavily. The clinical and demographic dataset used in this paper comprises features that need preprocessing operations such as labeling encoding for features that are categorical and feature scaling on others to set them in a prime state for model usage. These processes ensure consistency and strengthen the generalization ability of the models for different conditions. Using a Voting Classifier, the strengths of Random Forest and Gradient Boosting models are combined. These base models were tunned using RandomizedSearchCV for optimal hyperparameters and on balanced distributions split into train set and test set. Aggregating the predictions used here is a form of soft voting, improving overall predictive performance over the rest without the tedious task of human feature engineering. Among these, techniques such as confusion matrix, ROC curves, and feature importance analysis provide high comprehensiveness on the predictability accuracy, sensitivity, specificity, and interpretability of models. The results show high performance for the ensemble model with all the metrics, which confirms its robustness in identifying people with and without a heart problem. Additional visualizations like heatmaps and ROC curves provide a deeper insight into the most crucial features behind the predictions. In addition, a real-time prediction pipeline is implemented to demonstrate practical applicability to new cases. It is a finding that explains the role of machine learning in medical diagnostics in which a non-invasive and automated method applied for early detection of heart diseases, which focuses on solitary effort to bridge key gaps into medical applications of AI in healthcare.

In this paper, a new approach to specifying the closure of subsets in $ J^{E} $ is proposed, based on the concept of $ \mathcal{I} $-convergence of sequences. The notion of fuzzy $ \mathcal{I} $-closure is defined using this convergence, and from it, the fuzzy $ \mathcal{I} $-topology is introduced. Moreover, the concepts of continuity and $ \mathcal{I} $-continuity in fuzzy topological spaces are examined. A comparison between the proposed $ \mathcal{I} $-topology and the classical fuzzy topology is presented, highlighting certain fundamental properties and structural differences. This work contributes to the generalization of fuzzy topological structures by extending the role of sequence convergence through the use of $ \mathcal{I} $-convergence. Furthermore, a practical example illustrating temperature control in an industrial furnace is provided through the application of $ \mathcal{I} $-convergence of fuzzy sequences.

Anthropogenic emissions are responsible for deteriorated air quality and accelerated climate change in developing countries like India. The current trajectory of emissions is expected to further degrade air quality, potentially leading to increased warming levels by the end of the century, posing severe consequences for public health. In this study, we analyzed two scenarios using the GAINS-model framework - the business-as-usual (BAU), relying on existing air pollution control policies and measures, and the sustainable development scenario (SDS), integrating advanced air pollution control policies and measures, aiming to contain the global temperature increase below 2°C by 2100. We estimated the health burden attributable to ambient air pollution in BAU and SDS scenarios, segregated into regional and sectoral emissions in India for the years 2030 and 2050. Under the BAU scenario, premature mortality and disability-adjusted life-years (DALYs) are projected to increase from 0.72 million (95% CI: 0.53-0.89) and 24.8 million (15.4-30.5) in 2015 by 9.7% and 2.4% in 2030, respectively. In 2050, mortality and DALYs are projected to further increase to 0.88 million (0.75-1.01) and 26.2 million (22.8-29.6). At the sub-national level, states with a low Socio-demographic Index (SDI) are expected to possess majority (49-53%) of the health burden. However, if India follows the SDS scenario, 0.16 million (0.14-0.18) lives and 3.7 million (3.2-4.3) DALYs can be avoided in 2030. The corresponding benefits in 2050 will be 0.34 million (0.29-0.39) lives and 8.4 million (7.1-9.7) DALYs, respectively, relative to the BAU scenario. Our results reveal that states with a high SDI would experience the most significant benefits (15% and 26% for mortality & 26% and 44% for DALYs in 2030 and 2050), as compared to middle and low SDI states. The findings underscore the importance of immediate adoption of cost-effective and advanced technologies driven by sustainable development policies is imperative to mitigate air pollution and climate change simultaneously. A stronger mandate to revise the environmental standards and health policies is necessary to maximize health benefits in India. 

Blockchain, the latest distributed ledger technology, has brought paradigmatic shift to the mode of transaction and communication recently. None of the arenas/sectors/organisations is untouched of the technology globally. Recently, the appliance of the distributed ledger technology into digital currency astonished the global community. The applications, apprehension, risks and prospective of technology, thus, have been analysed periodically. In this article, the authors try to understand the potential scope, complexity and legal dynamics of blockchain in the Indian scenario, intermittently. The basic sources such as reports and documents of NITI Aayog, Working Paper of Ministry of Information and Technology as well as reports of Finance Ministry are taken to start with. Moreover, guidelines and press-releases of the Reserve Bank of India, various judgements of the Indian Supreme Court and Ministers’ statements in the Indian Parliament and various initiatives of states have also been taken as references to critically comprehend the convolution of blockchain in the Indian context.

The Government of India preferred legislative lever of public policy over economic incentives, funding support, public awareness campaigns levers by enacting the Mental Health Care Act (MHCA) in the year 2017. MHCA 2017 mandates for mental healthcare and services for persons with mental illness and to protect, promote and fulfil the rights of such persons during delivery of mental healthcare and services. MHCA 2017 is supposed to bring socio-cultural and structural changes to mental health issues in India. This article seeks to identify the various issues and challenges in the implementation of the Act at the macro level, specifically in the areas of prevention of mental morbidities, treatment gaps, reintegration and rehabilitation of the patient. It also suggests the way forward to remove the identified barriers.

This study examines the impact of career and psychosocial mentoring on the role efficacy and organisational role stress of employees. Survey methodology was used to test the hypotheses. The sample was randomly selected amongst the employees working in the IT sector in Delhi and National Capital Region (NCR), India. A total of 220 complete responses were received where 89 employees did not have any mentor and 131 employees were mentored. A pilot study was carried out to develop valid data items for the survey and to test the reliability and internal consistency. The findings indicate that mentoring plays a significant role in improving role efficacy and in reducing organisational role stress in the case of mentored employees as compared to non-mentored employees.

This paper is an attempt to broaden the discussion about the prevention of domestic violence against women informed by a rights-based strategy.Specifically, the study discusses the critical elements of a human rights framework to reduce domestic violence, present research findings on the prevalence and correlates of domestic violence in intimate relationships and explore strategies for the prevention of domestic violence on the basis of research and analysis.The study suggests that domestic violence needs to be resituated in the broader social transformation of society and that domestic violence should be conceptualized as violation of a woman's most basic right.The strength of a rights-based strategy is that it meshes formal treaty doctrines with grassroots activism and critiques of power.The study suggests that right to housing and right to property and inheritance are critical and most fundamental for any strategy in the prevention of domestic violence.Importance of immovable assets and social support is significant in making a difference to the incidence of violence, Changing norms of acceptability of violence in the family is critical to reduce inter-generational transmission of violence, male attitudes and society's attitudes also need to be changed in this regard.Since prevention of domestic violence requires fundamental changes in attitudes and behavior, it confronts societal and individual resistance to change, Support structures could be both within the family and all stakeholders who can both help in changing attitudes and in helping women acquire immovable assets.This calls for creative community involvement, shared responsibilities, and collective action with the goals to challenge the patriarchal assumptions of power and control and entitlement to women.